smol-podcaster

We use smol-podcaster to take care of most of Latent Space transcription work. What it will do for you:

• Generate a clean, diarized transcript of the podcast with speaker labels and timestamps
• Generate a list of chapters with timestamps for the episode
• Give you title ideas based on previous ones (modify the prompt to give examples of your own, it comes with Latent Space ones)
• Give you ideas for tweets to announce the podcast

Environment Setup

Activate virtualenv with

source venv/bin/activate

Install dependencies with

pip install -r requirements.txt

Make a copy of the .env.sample and replace it with your keys:

mv .env.sample .env

Run with web UI + background runs

If you want to run a bunch in parallel (or remotely) you can use the web UI + celery. Before running, you'll need a broker for celery (I use RabbitMQ).

If you have honcho installed, simply run honcho start, otherwise run each command manually:

celery -A tasks worker --loglevel=INFO
flask --app web.py --debug run

Then simply go to localhost:5000 and fill out the form. The files will be saved locally as /podcast-results just like the cli version.

Run from CLI

To run:

python smol_podcaster.py AUDIO_FILE_URL GUEST_NAME NUMBER_OF_SPEAKERS

The URL needs to be a direct download link, it can't be a GDrive. For files <100MB you can use tmpfiles.org (e.g. https://tmpfiles.org/dl/4338258/audio.mp3), otherwise Dropbox. For example:

python smol_podcaster.py "https://dl.dropboxusercontent.com/XXXX" "Tianqi" 3

Or, if you want to use a local file (with absolute or relative paths), use the following:

python smol_podcaster.py audio_sample.mp3 "test" 1

Or, use ~/Downloads/audio_sample.mp3 for file.

The script will automatically switch https://www.dropbox.com to https://dl.dropboxusercontent.com in the link.

Optional flags:

• --transcript_only will generate only the transcript without any of the show notes
• --generate_extra will also create tweets and title ideas

Audio / Video Sync

If you use smol-podcaster to transcribe both your audio and video files, you can create chapters based on your audio ones, put them in the form, and create a new list that matches the video transcript for YouTube. Usually audio and video have different lengths because less pauses are edited, so re-using the audio timestamps in the video doesn't work.

For example:

Timestamp: [00:10:00] Talking about Latent Space

Audio Transcript: [00:10:00] We love talking about Latent Space

Video Transcript: [00:12:05] We love talking about Latent Space

Will return you new chapters where the timestamp would be [00:12:05] Talking about Latent Space

This is based on string similarity, not hard-matching so don't worry about Whisper's mistakes.

License

MIT License